How to Build Self-Improving AI Agents Using Langfuse
Learn how to create AI support agents that continuously improve through feedback loops using Langfuse observability. A technical guide to building autonomous systems that learn from interactions.
Building Transparent AI Agents with Audit Trails and Human Gates
Technical guide to implementing traceable AI decision-making with comprehensive audit logging and human oversight checkpoints for accountable autonomous systems.
OpenClaw Exposes Critical Prompt Injection Flaw in AI Agents
Security researchers demonstrate how hidden prompt injections in code repositories can hijack AI coding agents like Cline, exposing critical vulnerabilities in agentic AI systems.
4 AI Safety Alignment Methods: Building Trustworthy AI Systems
From RLHF to Constitutional AI, these four technical approaches aim to prevent AI systems from lying, manipulating, or causing harm—critical foundations for trustworthy synthetic media.
VAEs Explained: The Hidden Engine Powering Modern AI Video
Variational Autoencoders compress reality into mathematical latent spaces, enabling everything from Stable Diffusion to AI video generation. Here's how the Bayesian math actually works.
Self-Generated Examples Boost LLM Reasoning Performance
New research reveals that LLMs reason better using their own examples rather than human-provided ones, suggesting the process of generation matters more than example quality.
Survey: How Narrative Theory Shapes LLM Story Generation
New survey examines how classical narrative frameworks are being integrated with large language models to improve automatic story generation and comprehension capabilities.
Proxy State Evaluation: Scaling Verifiable Rewards for AI Agents
New research proposes proxy state-based evaluation for multi-turn tool-calling LLM agents, addressing the challenge of scalable reward verification in complex agentic workflows.
New Research Maps Theoretical Limits of AI Data Contamination
Researchers establish mathematical framework for understanding how generative AI models can survive training on contaminated data, offering crucial insights for maintaining synthetic media quality.
Benchmarking Uncertainty Metrics in LLM-Based Assessment Systems
New research introduces a comprehensive benchmark for evaluating how well LLMs can quantify their own uncertainty when grading, with implications for AI reliability and trustworthy automated systems.
Anthropic's $6.4B Revenue Share Deal with Tech Giants Revealed
Anthropic may share up to $6.4 billion with Amazon, Google, and Microsoft by 2027 through cloud partnership agreements, revealing the massive financial stakes in enterprise AI infrastructure.
World Models Explained: The AI Architecture Reshaping Video
World models enable AI to simulate reality by learning internal representations of environments. This foundational architecture powers next-gen video generation, robotics, and autonomous systems.